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Analysis of energy consumption and greenhouse gas emissions trend in China, India, the USA, and Russia

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Abstract

With the growth of industries and population, the need for energy consumption has increased, which has inevitably increased greenhouse gas emissions. Further use of fossil fuel for energy consumption exacerbates the situation making it one of the major issues for climate change. China, India, the USA, and Russia are the world’s leading countries in energy consumption and emissions and are responsible for climate change. These countries account for 54% of carbon dioxide (CO2) emissions in the global environment. This paper investigates the energy consumption of China, India, the USA, and Russia and its trend in greenhouse gas emissions. Using four available datasets from 1980 to 2018 for China, India, USA, and 1992 to 2018 for Russia, we employed three advanced machine learning algorithms (support vector machine, artificial neural network, and long-short term memory) and verified its predicted capability with actual greenhouse gas emissions. The obtained results were evaluated with three statistical metrics (route mean square, mean absolute percentage error, and mean bias error). The predicted results with three machine learning algorithms were very close to actual greenhouse gas emissions. Besides, we forecasted the trend of greenhouse gas emissions in these countries from 2019 to 2023. The forecasted results with the long-short term memory model confirm an increase in CO2, methane, and Nitrous oxide (N2O) emissions in the case of China and India; in contrast, the results indicate a slowdown of CO2, methane, and N2O emissions in the USA and Russia.

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Acknowledgements

We acknowledge the participants of the study for their valuable contribution. The authors thank the reviewers for their comments, which improved the final version of this paper.

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Correspondence to C. Shuai.

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Ahmed, M., Shuai, C. & Ahmed, M. Analysis of energy consumption and greenhouse gas emissions trend in China, India, the USA, and Russia. Int. J. Environ. Sci. Technol. 20, 2683–2698 (2023). https://doi.org/10.1007/s13762-022-04159-y

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